图像模型随着时间推移生成被部分吃过的墨西哥卷饼。
The Generative Burrito Test

原始链接: https://www.generativist.com/notes/2025/Nov/25/generative-burrito-test.html

最初灵感来自2023年那张骑马宇航员的梗图。但我觉得Simon的鹈鹕基准测试更能让我保持这个想法,即使他们测试的是不同的模态。卷饼显然比鹈鹕和骑术荒诞更重要。起初,我很惊讶它无法很好地复制图像,因为我假设训练数据中会有很多类似的例子(不像那骑术荒诞)。但我觉得这有点奇怪的概念,因为所有的食材都会被挤压、捣碎和凝固。所有图像都使用fal的默认设置生成。显然,你可能可以通过更好的提示来改善它,但这需要大量工作,感觉像是作弊。

一个 Hacker News 的讨论集中在图像模型生成部分吃过的墨西哥卷饼的图片上,相关展示在 generativist.com。用户普遍认为“Nano Banana Pro”(NB Pro)产生最逼真的结果,实际上看起来像有人咬了一口的墨西哥卷饼,与其他模型不同,其他模型生成的图像更像是摆拍的食物摄影。 一些评论者注意到一张最初归因于 base SD 1.5 的图像出人意料的高质量,并认为它很可能是 RealisticVision 等微调版本。其他人讨论了墨西哥卷饼风格的地区差异(斯科茨代尔 vs. 索诺兰),并表达了希望看到视频生成结果。甚至有人建议将“墨西哥卷饼基准”作为评估这些模型的潜在行业标准。最终,这些图像引发了食欲,并幽默地承认了它们诱食的力量。
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原文

This was originally inspired by the horse riding astronaut meme way back in 2023. But I think Simon's Pelican benchmark is what keeps the idea alive for me, even though they are testing different modalities. Burritos are obviously more important than both pelicans and equestrian absurdism.

Also, I was initially surprised that it couldn't replicate the image well because I assumed there would be plenty of similar examples in the training data (unlike said equestrian absurdity). But I think it's a bit of a weird concept because all the ingredients get smushed and smashed and congealed.

All images generated using fal defaults. Obviously you can probably prompt it better, but that's HIL effort, and feels like cheating.

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